The Social Science Genetic Association Consortium (SSGAC) is a research network that provides a platform for large-scale, interdisciplinary collaborations on genome-wide association studies of behavioral phenotypes. Because credible results often require large discovery samples, a primary function of the SSGAC is to conduct genome-wide association studies conducted in different datasets and then rigorously combine the results. To this end, the SSGAC also promotes the collection of harmonized and well-measured phenotypes across datasets and the development of new methodological tools. The SSGAC is committed to disseminating its research findings, as well as accompanying software and manuals, to the research community. The overarching goal of this proposal is to maintain and develop the core infrastructure of the SSGAC. The resources produced by the SSGAC will continue to serve the field of behavioral genomics in general, and the behavioral genomics of aging in particular. The Specific Aims are: ? Conduct genome-wide association studies of health and aging-relevant behavioral phenotypes in much larger samples that have now become available. Currently planned studies include analyses of educational attainment, general risk tolerance, and life satisfaction, all in unprecedentedly large samples. In addition, we will undertake large-scale studies of additional phenotypes as data becomes available. ? Facilitate researchers' use of SSGAC results by (a) maintaining published results on the SSGAC's website, and (b) providing participating datasets with certain unpublished results, in accordance with the SSGAC's Data Availability Policy. ? Develop a ?Repository of Polygenic Scores,? which are variables constructed from genetic data that are useful to researchers. We will set up the Repository to provide polygenic scores to participating datasets. ? Update and expand the Repository as new genetic-association results become available. We will also welcome any new datasets who wish to participate. Each release will be accompanied by documentation that clearly describes methods used and the underlying data. ? Create and disseminate software that will allow datasets to create their own polygenic scores, using the same harmonized methodology as the Repository but without needing to share their genetic data with the SSGAC. The software will be publicly available on a GitHub repository featuring a Q&A forum where potential users can ask questions. We will also prepare user-friendly software manual.